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User interfaces T

he user interface (UI) is the next battlefi eld for consumer electronics

manufacturers. With only relatively minor diff erences in the features they off er, OEMs have realized that consumers no longer decide between devices solely on what they do. Instead, they turn to the industrial design and UI of a product. T e UI defi nes the personality

of a device. T e look-and-feel creates emotional ties between people and their devices and binds their loyalty. T e truth of this can be seen in everything from the iPhone to kitchen appliances: the decision to buy a particular toaster is less about the toast than how the product looks on the counter. UI features that don’t work

well aren’t used, so devices must work out of the box and adapt to the specifi c preferences of individuals. To achieve this, devices need to become more intelligent and will require more processing power and memory.

Predictive technology Predictive intelligence is the ability of a system to predict a user’s intentions by understanding the diff erent choices the person can make and which is the most likely to be selected. It can be active or passive. For example, auto- correction in SMS texting can actively replace misspelled words. Predictive intelligence can

introduce complexity to a system, however. For a UI with fi xed options, such as controlling an infotainment console, a

Alvin Wong is vice president of marketing and business development in the programmable system solutions group at the chip-maker Spansion


command-and-control speech recognition interface supports a limited vocabulary to provide low latency and high reliability. Natural language processing, where a user can talk as if to another person, not only requires advanced storage and computing capabilities but also needs to adapt to individual users to improve accuracy.

Emotion, distraction T e more data the system can collect about a user, the more confi dently it can predict what the user wants. Today, predictive technology assumes that people are consistent in their interactions. In reality, a person’s emotions and distractions aff ect their choices. Consider a car’s speech-recog-

nition system. A person who is angry when getting into the car is likely to speak more quickly or yell at the car, resulting in lower recognition accuracy and further frustration. A system that can ad- just its behaviour to compensate for agitated responses is needed. For example, the system could apologize for not understanding the user and ask for help by sug- gesting that the user roll up the window. By proactively adapting to the situation, the system can begin to help calm the user, thereby improving accuracy. Predicting the user’s state of

mind is extremely complex. To maximize accuracy, multiple sources are needed to determine the user’s intent and emotion. T e camera on a mobile phone could recognize the face of a user but also his or her current emotional state from facial expressions and body language. T e touchscreen could sense an urgent or agitated state by how hard and how quickly a user is pressing keys. Similarly, a speech

Alvin Wong looks forward to advanced technologies which will guess our intentions, enabling us to interact more instinctively with the devices of the future

‘The touchscreen could sense an urgent or agitated state by how hard and how quickly a user is pressing keys...’

recognition system could monitor changes in the user’s voice and mood even as it adjusts for them. In addition, these systems could co-ordinate their results to improve accuracy. Determining a user’s emotional

state has further applications beyond just improving the accuracy of the UI. Advertisers would also be likely to pay a premium to place their ads when a person is more engaged, such as when talking to friends over social networks. However, this requires

signifi cant complexity within the device and one challenge for OEMs is determining whether to implement intelligence processing on board a device or in the cloud.

Cloud processes In the cloud, processing is centralized over the network, allowing even systems with limited processing capabilities to support more advanced technologies. However, because data has to be sent over the network connection, there is noticeable latency. Also, IP networks may drop packets, resulting in unreliable responsiveness, and there are interoperability issues that must be addressed.

A wholly on-board implementa tion, in contrast, off ers high reliability with fast responsiveness, but at a higher equipment cost. For this reason, many automotive applications will employ a hybrid approach with on-board resources used

for functions such as operating air-conditioning or radio, with the cloud used for features that require substantial memory and processing capabilities, such as analysing user behaviour. T ese profi les can then be downloaded to the on-board system.

Rapid evolution Advanced system intelligence will require sophisticated analysis capabilities involving a mix of hardware accelerators, fl exible software algorithms and look-up tables. Systems will also need to be highly customizable and off er elegant error handling as well as being secure. Of course, numerous

technologies – graphics, encryption, digital signal processing, high-speed communications – have leveraged specialized hardware to offl oad tasks from the host processor. In the same way, OEMs

will need to rely on hardware integrated circuits (ICs) to keep pace with the leading edge of UI technology. Single-feature ICs will quickly evolve over the next fi ve to ten years into dedicated UI processors which will offl oad processing of multiple forms of recognition – speech, voice, image, facial, and emotion – from the applications processor. T ese will eventually be integrated into the general-purpose processor architecture. And with UI processors, OEMs will be able to introduce advanced features to provide greater ease of use.

LAND mobile March 2012

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